J. C. Briones1, V. Heras1, C. Abril2, and E. Sinchi11Universidad de Cuenca, Architecture and Urbanism Faculty, Av 12 de Abril & Agustín Cueva, Cuenca, Ecuador2Carleton University, 1125 Colonel By Drive, Ottawa, K1S 5B6, CanadaKeywords: Preventive conservation, Monitoring, Imagery classification, Support Vector Machines, Roof structures, morphological mathematicAbstract. The proper control of built heritage entails many challenges related to the complexity of heritage elements and the extent of the area to be managed, for which the available resources must be efficiently used. In this scenario, the preventive conservation approach, based on the concept that prevent is better than cure, emerges as a strategy to avoid the progressive and imminent loss of monuments and heritage sites. Regular monitoring appears as a key tool to identify timely changes in heritage assets. This research demonstrates that the supervised learning model (Support Vector Machines – SVM) is an ideal tool that supports the monitoring process detecting visible elements in aerial images such as roofs structures, vegetation and pavements. The linear, gaussian and polynomial kernel functions were tested; the lineal function provided better results over the other functions. It is important to mention that due to the high level of segmentation generated by the classification procedure, it was necessary to apply a generalization process through opening a mathematical morphological operation, which simplified the over classification for the monitored elements.Conference paper (PDF, 2005 KB)